This one felt more like a sysadmin pop quiz than a coding problem.
Start by narrowing down the time window of the failure using system metrics or recent changes, then filter the log file to that window and search for error patterns or exceptions. Use command-line tools like grep, awk, and tail to efficiently extract relevant lines without loading the entire file.
Pro tip: Before diving into logs, check if the issue is ongoing: run 'tail -f' on the log to see live errors, which can immediately reveal the problem. Also, consider using 'less' with search and filter capabilities for interactive exploration.
Check system resource usage (CPU, memory, disk) and recent changes (deployments, config updates) to narrow down potential causes and timeframes.
Identify when the failure started by looking at monitoring alerts or asking stakeholders, then focus on logs from that period.
Use grep, awk, sed, or tail to extract relevant lines based on timestamps, error levels (ERROR, WARN), or specific keywords like exceptions or stack traces.
Look for recurring errors, sudden spikes in log volume, or correlated events across multiple log lines to identify the root cause.
Confirm the issue by reproducing or checking related metrics, then take corrective action or escalate with precise findings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use bitwise XOR with a mask that has a 1 at the target bit position. This flips the bit while leaving all other bits unchanged. Explain the operation clearly and consider edge cases like invalid bit positions.
Pro tip: Mention that XOR is the standard idiom for toggling bits and that it works for both 0 and 1. Also, clarify how you would handle out-of-range bit positions to show robustness.
Confirm that you need to flip a single bit at a given position, changing 0 to 1 or 1 to 0, without affecting other bits.
Select XOR with a mask (1 << position) because XOR toggles the bit at that position.
Create the mask by left-shifting 1 by the bit position: mask = 1 << pos.
Compute the result as number ^ mask and return it.
Check if the bit position is valid (e.g., between 0 and 31 for 32-bit integers) and discuss behavior for invalid positions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I stared at it longer than I should have before spotting the classic two-thread, two-lock cycle where each thread holds one lock and waits on the other.
First, identify the lock acquisition order in the code and check for circular dependencies where two or more threads acquire locks in different orders. Then, propose a fix such as establishing a global lock ordering, using a single lock, or employing lock timeouts with retry logic. Explain the trade-offs of each solution.
Pro tip: Mention that deadlocks can also be prevented by using lock-free data structures or by acquiring locks with timeouts, but always consider the performance implications. Also, emphasize the importance of writing a unit test that reproduces the deadlock to verify the fix.
Trace the code to find two or more threads that acquire locks in different orders, creating a circular wait. Point out the specific lines where locks are acquired and released.
Describe the four necessary conditions for deadlock (mutual exclusion, hold and wait, no preemption, circular wait) and how they apply to the given code.
Suggest a solution such as enforcing a global lock ordering, using a single coarse-grained lock, or using tryLock with timeouts. Justify why the fix resolves the deadlock.
Compare the proposed fix with alternatives in terms of performance, scalability, and complexity. Mention potential issues like reduced concurrency or increased overhead.
Explain how to test the fix, such as writing a unit test that simulates concurrent access and ensures no deadlock occurs. Mention tools like thread sanitizers or stress tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The base case was fine, recursive traversal plus a hash map plus a heap for top-K.
Start by clarifying requirements (file types, word definition, case sensitivity, top-K size) and then outline a solution using a hash map for word counts and a min-heap for top-K. For the follow-up, discuss external sorting or streaming with distributed processing to handle data exceeding memory.
Pro tip: Mention that you would use a min-heap of size K to efficiently track the top-K words, and for large data, propose a MapReduce approach with combiners to reduce network traffic.
Ask about file types, word definition (e.g., alphanumeric, case sensitivity), and constraints like K and memory limits. This ensures you build the right solution.
Traverse the directory tree recursively, read each file, tokenize words, and update a hash map of word frequencies. Then use a min-heap of size K to extract the top-K frequent words.
Discuss time complexity: O(N) for reading and counting, O(N log K) for heap operations, where N is total words. Space complexity: O(U) for unique words, which may be large.
Propose solutions for data exceeding memory: external sorting, streaming with approximate algorithms, or distributed processing (e.g., MapReduce) with local aggregation and combiners.
Compare approaches: exact vs. approximate, memory vs. speed, and centralized vs. distributed. Highlight when each is appropriate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.